Quantum Code Weaver Engine

A distributed, graph-based coding engine transforming software development from conversation to state-space search.

System Architecture

State-Driven Execution

System Status

Performance Metrics

Architecture Components

The Brain

Local LLM Orchestrator responsible for verification, scoring, and routing.

  • Zero heavy generation
  • JSON-in, JSON-out only
  • Hot state processing

The Muscle

Cloud LLM Workers for high-volume, parallel code generation.

  • Stateless functions
  • Parallel execution
  • AST-compliant output

Nervous System

Redis-based Pub/Sub queue implementing the Hot Event Loop.

  • Async decoupling
  • Task & result queues
  • Real-time processing

Long-Term Memory

Neo4j graph database storing the Verification Graph.

  • Nodes = Verified states
  • Edges = Derivation paths
  • No failed attempts stored

Orchestrator Scaffolding

class Orchestrator:
    def __init__(self, redis_host: str = "localhost", redis_port: int = 6379):
        self.redis_client = redis.Redis(host=redis_host, port=redis_port)
        self.neo4j_driver = GraphDatabase.driver("bolt://localhost:7689")
        self.threshold_score = 0.8
        # Async initialization complete
    
    async def poll_results(self) -> List[Dict[str, Any]]:
        """Continuously poll Redis for worker results"""
        while True:
            result = self.redis_client.rpop("queue:results")
            if result:
                yield json.loads(result)
            await asyncio.sleep(0.01)
    
    def evaluate_candidate(self, candidate: Dict[str, Any]) -> float:
        """Score code candidate (stubbed with LLM integration)"""
        return random.uniform(0, 1)  # Placeholder for LLM scoring
    
    async def update_graph(self, candidate: Dict[str, Any], score: float):
        """Commit to Neo4j if score exceeds threshold"""
        if score > self.threshold_score:
            with self.neo4j_driver.session() as session:
                session.run(...)  # Graph update logic